总结25条MLOps模型集成部署的架构指南，助团队少走弯路。
Architecturally Significant MLOps Guidelines for ML Model Integration and Deployment: a Gray Literature Review
- 从103篇非正式资料中提炼出25条可落地的架构建议
- 指南涵盖模型集成与部署五大类别，影响系统整体设计
- 适合正在搭建或优化MLOps系统的研发和工程团队
背景：尽管机器学习运维（MLOps）应用日益广泛，但团队常因缺乏整合的架构指导而采取随意做法。建立一套能指导模型集成与部署的参考框架对社区大有裨益。目标：本文旨在提供一份全面的MLOps系统中模型集成与部署的架构重要性指南概述。方法：通过灰文献调研分析103个网络来源，运用主题分析法将实践归纳为推荐准则。结果：我们提出了25条具有架构影响力的MLOps指南，分为五大类别，并阐述其对整体系统架构的影响。结论：本研究为当前MLOps实践提供了系统性指南，可支持研究人员与从业者在构建和优化其MLOps系统时更高效地实现模型集成与部署。
原文摘要 · Abstract (English)
Context. Despite the growing adoption of Machine Learning Operations (MLOps), teams often approach MLOps projects in an ad hoc manner due to the lack of consolidated architectural guidance. The community would benefit from a reference that synthesizes knowledge to inform the architectural design of MLOps systems, especially regarding the integration and deployment of ML models. Objective. In response, our goal is to provide a comprehensive overview of architecturally significant guidelines for the integration and deployment of ML models in MLOps systems. Method. We conduct a gray literature review of 103 web sources to analyze state-of-practice knowledge on MLOps model integration and deployment. We then apply thematic analysis to synthesize these practices into recommended guidelines. Results. We contribute a collection of 25 architecturally significant MLOps guidelines for model integration and deployment, organized into five categories, and describe their impact on the overall system architecture. Conclusion. Our results serve as an overview of state-of-practice MLOps guidelines to support researchers and practitioners with the integration and deployment of ML models in their MLOps systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。